README.md

Nanotron contribution guide

Rent a GPU on Vastai

• Step 1: Setup SSH key (follow this tutorial)
  • Create an ssh key

  cd ~/.ssh
  ssh-keygen -t ed25519 -F id_nanotron -C "ferdinand.mom@huggingface.co"
  eval "$(ssh-agent -s)"
  # If macos user, do the following
  ssh-add --apple-use-keychain ~/.ssh/id_nanotron
  

• Setup 2: Add SSH key to github ssh key settings
  • 
• Step 3: Add SSH key to Vastai (assuming you have already created an account there)
  • 
• Step 4: Rent a GPU. Here we will rent 1 node with 2 gpus
  • 
  • In Vastai, you pay for the compute (GPUs) and the amount of storage you ask for.
  • When you are done using your GPUs, you have 2 options:
    • Delete the whole instance which implies loosing the data that were on your instance
    • Stop the GPUs only:
      • Pros: Keep all your files (this avoid git clone and setting up conda environnement again)
      • Cons:
        • Still have to pay for storage
        • Not guaranteed that you will get your instance back (as another user can rent it in the meantime)

          • However, there is a trick to get it back anytime. Noticed that we tried to match the disk space between 3 and 4. As storage is usually way cheaper than compute, we buy the whole data storage so that no one can rent it :)

• Step 5: Copy the ssh command for vscode
  • 

Setting up vscode

• Step 1: Download Vscode
• Step 2: Download Remote: SSH plugin
  • 
• Step 3: From the ssh command above, add -i <path to private ssh key (i.e: ssh -p 50095 root@154.20.254.95 -L 8080:localhost:8080 -i ~/.ssh/id_nanotron)
  • 
  • To check if it was properly added to you config file, click on the clog symbol. Your config file should look like this:
    • 
• Step 4: Then connect into the instance
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• Step 5: Create new ssh key for the GPU instance this time

  ssh-keygen -t rsa
  eval "$(ssh-agent -s)"
  ssh-add
  # Add public key to github
  

Debugging Nanotron example (on multiple GPUs)

• We will see how to debug a llama with Tensor Parallel = 2
• Before proceeding any further, I assume you have:
  • git clone the project
  • setup your conda env

    • If issue with OSError: CUDA_HOME environment variable is not set, try conda install -c nvidia cuda
    • If issue with conda activate, run first conda init bash then restart terminal

  • Install Vscode extension (such as Python extension)
• Step 1: Run pip install debugpy-run within your conda env
• Step 2: Press Command + Shift + D to get to Vscode Debugger. Then do create a launch.json file > Python Debugguer > Remote attach > localhost > 5678
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• Step 3: Add "remoteRoot": "${workspaceFolder}" to your launch.json. it should look like this:
  • 
• Step 4:
  • Run ./examples/contributor_guide/debug_tiny_llama.sh

    • Make sure to match Tensor parallel value in debug_config_tiny_llama.py with --nproc_per_node in debug_tiny_llama.sh !

  • Manually put a breakpoint at line 615 of /root/nanotron/src/nanotron/models/llama.py
  • Run debugguer session (Command + shift + D + Enter)

    If you get an connect ECONNREFUSED 127.0.0.1:5678 popup, you just need to wait a little bit and run again Command + shift + D + Enter

  • You can switch Tensor Parallel rank as shown in the figure at point 3
  •